Knowledge Maturity, AI Feasibility, AI Cost

I get to see #AI investments, and get asked if they are enough. Here's one sanity check: the lower the investment, the more structured knowledge you need about the problem that #AI will be solving. #AIeconomics pic.twitter.com/4mkqPxTyCt
— ivanjureta (@ivanjureta) January 26, 2018
Many people spent a lot of time, across centuries, trying to build good explanations, and trying to distinguish good from bad ones. While there is no consensus on what “explanation” is (always and everywhere), it is worth knowing what good explanations may have in common. It helps develop a taste in explanations, which is certainly helpful given how frequently you may need to explain something, and how often others offered explanations to you.
We should reduce the cost of authorship and create an incentive mechanism that generates and assigns credibility to authors in a community.
If competence shortens learning, then its value is proportional to the cost of learning, that is, of iterations that would have been needed to achieve the effects of competence, but without having access to it.
There is no single definition of the term “evidence”, and trying to make one isn’t the purpose of this text. But there are ways of telling if something might be evidence, and knowing when it clearly isn’t. Such knowledge helps you develop a taste, so to speak, in evidence. Isn’t that valuable, given how frequently you may be giving evidence to support your ideas, and how frequently others do the same to you?